SEED-UMI: Sharing the Exoskeleton between human and robot for onE-to-one Dexterous demonstration

2026-09-10

AI Summary

为解决灵巧手模仿学习中接触丰富演示难以收集的问题，SEED-UMI通过让人类和机器人共享同一外骨骼，提高了数据收集效率和任务成功率。

Abstract

Imitation learning for dexterous hands is bottlenecked by the difficulty of collecting contact-rich demonstrations that transfer faithfully to the robot. Prior wearable-exoskeleton systems record only on the human side and retarget via open-loop mappings calibrated in free space, which degrade under contact. We present SEED-UMI, a framework in which both the human and the robot wear the same exoskeleton: joint encoders become a physically shared measurement, and wrist cameras mounted to the exoskeleton observe the same outer mechanism during both human data collection and robot policy rollouts. This turns retargeting into paired cross-embodiment supervision and lets policies train directly on raw exoskeleton-centric wrist images, without segmentation or inpainting. On five contact-rich tasks, SEED-UMI achieves 3.0x greater data collection efficiency than exoskeleton-based teleoperation and a 70.0% average rollout success rate.

Problem

• imitation learning
• dexterous hands
• contact-rich demonstrations
• wearable exoskeletons

Innovation

• shared exoskeleton
• cross-embodiment supervision
• wrist camera
• raw image training
• contact-rich tasks